Lectureture Plans

The following plan is tentative and subject to changes.

Livestream Lectures: A zoom link will be sent out before each lecture. Recorded Lectures: will be available on Canvas.

Note: the numbers in the parenthesis represents the corresponding sections of the textbook.

  • Lecture 01 (Jan 08): Basic probability
  • Lecture 02 (Jan 11): Binomial and Poisson distributions (1.2, 1.3, 1.4, 1.6, 1.7)
  • Lecture 03 (Jan 13): Poisson distributions (1.7), Distribution and density functions(2.1)

Chapter 2 Continuous random variables

  • Lecture 04 (Jan 15): Moment generating functions. Exponential Gamma, and Beta distributions (2.2, 2.3, 2.4, 2.6, 2.14) slides
  • Lecture 05 (Jan 20): Normal and lognormal distributions slides
  • Lecture 06 (Jan 22): Chebyshev’s inequality (2.8), Law of large numbers and central limit theorem (2.9, 2.10) slides
  • Lecture 07 (Jan 25): Transformations and the delta method (2.12, 2.13) slides

Chapter 3 Multivariate random variables

  • Lecture 08 (Jan 27): Joint CDF, independence (3.1, 3.2) slides
  • Lecture 09 (Jan 29): Conditional density (3.3) slides
  • Lecture 10 (Feb 01): Correlation and linear regression (3.4) slides

  • Lecture 11 (Feb 03): Correlation and linear regression (3.4), Bivariate normal distribution (3.5) slides

  • Lecture 12 (Feb 05): Joint density upon transformations (3.6), Optimal portfolio allocations (3.8) slides
  • Lecture 13 (Feb 08): Multidimensional random vectors (3.10) slides

Midterm test (Feb 10)

  • Lecture 14 (Feb 10): Review for midterm

Chapter 4 Four important distributions in statistics

  • Lecture 15 (Feb 12): Chi-square distribution (4.2), t- and F-distributions (4.3, 4.4) slides

Chapter 6 Parameter estimation

  • Lecture 16 (Feb 15): Statistics as inverse probability (6.1), Method of moments (6.2), Method of Quantiles (6.3)slides
  • Lecture 17 (Feb 17): Statistical properties of an estimator (6.4) slides
  • Lecture 18 (Feb 19): Linear estimation (6.5), Estimation of variance and correlation coefficient (6.6), Least squares (6.7) slides
  • Lecture 19 (Feb 22): Least squares (6.7) slides
  • Lecture 20 (Feb 24): Maximum likelihood (6.10) slides

Chapter 7 Hypothesis testing and confidence intervals

  • Lecture 21 (Feb 26): Hypothesis testing (7.1, 7.2) slides
  • Lecture 22 (Mar 01): Confidence interval
  • Lecture 23 (Mar 03): Three asymptotic tests and confidence intervals

Chapter 8 Linear model and its extensions

  • Lecture 24 (Mar 05): The Gauss-Markov theorem
  • Lecture 25 (Mar 08): Statistical inference with lilear models
  • Lecture 26 (Mar 10): Review